text: # coding: utf-8
  from __future__ import division, print_function
  
  import models
  import data
  
  import theano
  import sys
  from io import open
  
  import theano.tensor as T
  import numpy as np
  
  MAX_SUBSEQUENCE_LEN = 200
  
  def to_array(arr, dtype=np.int32):
      # minibatch of 1 sequence as column
      return np.array([arr], dtype=dtype).T
  
  def convert_punctuation_to_readable(punct_token):
      if punct_token == data.SPACE:
          return " "
      else:
          return punct_token[0]
  
  def restore_with_pauses(output_file, text, pauses, word_vocabulary, reverse_punctuation_vocabulary, predict_function):
      i = 0
      with open(output_file, 'w', encoding='utf-8') as f_out:
          while True:
  
              subsequence = text[i:i+MAX_SUBSEQUENCE_LEN]
              subsequence_pauses = pauses[i:i+MAX_SUBSEQUENCE_LEN]
  
              if len(subsequence) == 0:
                  break
  
              converted_subsequence = [word_vocabulary.get(w, word_vocabulary[data.UNK]) for w in subsequence]
  
              y = predict_function(to_array(converted_subsequence), to_array(subsequence_pauses, dtype=theano.config.floatX))
  
              f_out.write(subsequence[0])
  
              last_eos_idx = 0
              punctuations = []
              for y_t in y:
  
                  p_i = np.argmax(y_t.flatten())
                  punctuation = reverse_punctuation_vocabulary[p_i]
  
                  punctuations.append(punctuation)
  
                  if punctuation in data.EOS_TOKENS:
                      last_eos_idx = len(punctuations) # we intentionally want the index of next element
  
              if subsequence[-1] == data.END:
                  step = len(subsequence) - 1
              elif last_eos_idx != 0:
                  step = last_eos_idx
              else:
                  step = len(subsequence) - 1
  
              for j in range(step):
                  f_out.write(" " + punctuations[j] + " " if punctuations[j] != data.SPACE else " ")
                  if j < step - 1:
                      f_out.write(subsequence[1+j])
  
              if subsequence[-1] == data.END:
                  break
  
              i += step
  
  def restore(output_file, text, word_vocabulary, reverse_punctuation_vocabulary, predict_function):
      i = 0
      with open(output_file, 'w', encoding='utf-8') as f_out:
          while True:
  
              subsequence = text[i:i+MAX_SUBSEQUENCE_LEN]
  
              if len(subsequence) == 0:
                  break
  
              converted_subsequence = [word_vocabulary.get(w, word_vocabulary[data.UNK]) for w in subsequence]
  
              y = predict_function(to_array(converted_subsequence))
  
              f_out.write(subsequence[0])
  
              last_eos_idx = 0
              punctuations = []
              for y_t in y:
  
                  p_i = np.argmax(y_t.flatten())
                  punctuation = reverse_punctuation_vocabulary[p_i]
  
                  punctuations.append(punctuation)
  
                  if punctuation in data.EOS_TOKENS:
                      last_eos_idx = len(punctuations) # we intentionally want the index of next element
  
              if subsequence[-1] == data.END:
                  step = len(subsequence) - 1
              elif last_eos_idx != 0:
                  step = last_eos_idx
              else:
                  step = len(subsequence) - 1
  
              for j in range(step):
                  f_out.write(" " + punctuations[j] + " " if punctuations[j] != data.SPACE else " ")
                  if j < step - 1:
                      f_out.write(subsequence[1+j])
  
              if subsequence[-1] == data.END:
                  break
  
              i += step
  
  if __name__ == "__main__":
  
      if len(sys.argv) > 1:
          model_file = sys.argv[1]
      else:
          sys.exit("Model file path argument missing")
  
      if len(sys.argv) > 2:
          output_file = sys.argv[2]
      else:
          sys.exit("Output file path argument missing")
  
      use_pauses = len(sys.argv) > 3 and bool(int(sys.argv[3]))
  
      x = T.imatrix('x')
      
      if use_pauses:
      
          p = T.matrix('p')
  
          print("Loading model parameters...")
          net, _ = models.load(model_file, 1, x, p)
  
          print("Building model...")
          predict = theano.function(
              inputs=[x, p],
              outputs=net.y
          )
  
      else:
  
          print("Loading model parameters...")
          net, _ = models.load(model_file, 1, x)
  
          print("Building model...")
          predict = theano.function(
              inputs=[x],
              outputs=net.y
          )
  
      word_vocabulary = net.x_vocabulary
      punctuation_vocabulary = net.y_vocabulary
  
      reverse_word_vocabulary = {v:k for k,v in word_vocabulary.items()}
      reverse_punctuation_vocabulary = {v:k for k,v in punctuation_vocabulary.items()}
  
      input_text = open(sys.stdin.fileno(), 'r', encoding='utf-8').read()
  
      if len(input_text) == 0:
          sys.exit("Input text from stdin missing.")
  
      text = [w for w in input_text.split() if w not in punctuation_vocabulary and w not in data.PUNCTUATION_MAPPING and not w.startswith(data.PAUSE_PREFIX)] + [data.END]
      pauses = [float(s.replace(data.PAUSE_PREFIX,"").replace(">","")) for s in input_text.split() if s.startswith(data.PAUSE_PREFIX)]
  
      if not use_pauses:
          restore(output_file, text, word_vocabulary, reverse_punctuation_vocabulary, predict)
      else:
          if not pauses:
              pauses = [0.0 for _ in range(len(text)-1)]
          restore_with_pauses(output_file, text, pauses, word_vocabulary, reverse_punctuation_vocabulary, predict)
  